Publications

Automatic Goal Generation for Reinforcement Learning Agents

Carlos Florensa*, David Held*, Xinyang Geng*, Pieter Abbeel

In ICML, 2018.

Also in Deep Reinforcement Learning Symposium, NIPS 2017

We propose a method that allows an agent to automatically discover the range of tasks that it is
capable of performing in its environment. We use a generator network to propose tasks for the
agent to try to achieve, specified as sets of goal states.

Real-Time User-Guided Image Colorization with Learned Deep Priors

We propose a deep learning approach for user-guided image colorization. We system directly
maps a grayscale image, along with sparse, local user “hints” to an output colorization
with a deep convolutional neural network.

Deep Reinforcement Learning for Tensegrity Robot Locomotion

We collaborated with NASA Ames to explore the challenges associated with learning locomotion
strategies for tensegrity robots, a class of compliant robots that hold promise for future planetary exploration missions.
We devised a novel extension of mirror descent guided policy search to learn locomotion gaits for the SUPERball
tensegrity robot, both in simulationand on the physical robot.